Organic Photovoltaic Efficiency Predictor: Data-Driven Predictions of Power Conversion Efficiencies of Non-Fullerene Acceptor Organic Solar Cells

05 November 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


In the fabrication of organic solar cells, there has been a need for materials with high power conversion efficiencies (PCE). Scharber’s model is commonly used to predict efficiency, however it exhibits poor performance with new non-fullerene acceptor (NFA) devices (RMSE=2.53%). In this work, an empirical model is proposed that can be a more accurate alternative for NFA organic solar cells. Additionally, many screening studies use computationally expensive methods. A model based on using the semi-empirical simplified time-dependent density functional theory (sTD-DFT) as an alternative method can accelerate the calculations and yields similar accuracy. The models presented in this paper, referred to as Organic Photovoltaic Efficiency Predictor (OPEP) models, have shown significantly lower errors than previous models, with OPEP/B3LYP yielding errors of 1.53% and OPEP/sTD-DFT of 1.55%. The proposed computational models can be utilized for fast and accurate screening of new high-efficiency NFAs and donor polymer pairs.


non-fullerene acceptors
organic photovoltaics
organic solar cells
data-driven materials

Supplementary materials

Supporting Information
Details on Scharber’s, Imamura’s, and Alhabri’s models, description of descriptors analyzed, experimental dataset, OPEP models for B3LYP, sTD-DFT, and sTD-DFT Expanded trained on experimental PCE above 9% and on all ranges of PCE to predict FF, JSC, Voc, and PCE, figures: histogram of experimental FF, correlations between B3LYP and CAM-B3LYP, violin plots for all models examined across all ranges of PCE, performance of OPEP models trained on high PCE on all ranges of experimental PCE.

Supplementary weblinks


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